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数据来源：ArXiv Domain LLM Domain Papers1. FD-VAD: Semantic Endpoint Detection for Streaming Full-Duplex SpeechAbstract:Natural turn-taking in full-duplex voice interaction requires determining from partial speech whether a pause reflects hesitation or a completed conversational intent. Acoustic voice activity detection lacks this semantic information, while cascaded ASR-based endpointing introduces transcription dependence and additional processing stages. We formulate semantic endpoint detection ...

数据来源：HuggingFace Papers Latest Papers1. VFlash: Fast, Accurate Visual Planning via Flash Primitive DecodingAbstract:Models for physical AI tasks, such as robotics and autonomous driving, need not reason solely in language. They can use visual plans—points, bounding boxes, and trajectories—to represent spatial goals and anticipate motion. However, autoregressive vision-language models generate these plans one coordinate token at a time, making richer predictions increasingly costly. We introduc ...

数据来源：ArXiv Domain LLM Domain Papers1. baseVultra: Compact Nonlinear Recurrence with Reallocated CapacityAbstract:Nonlinear recurrent networks remain useful for streaming sequence modeling, yet gated cell design is still dominated by LSTM and GRU. We study whether recurrent computation can be reallocated away from gate transforms and toward candidate generation. We propose baseVultra, a compact recurrent cell that concentrates the dense recurrent budget on a single H x H candidate-generation ma ...

数据来源：HuggingFace Papers Latest Papers1. OneStreamer: Unifying Perception, Memory, and Proactive Response in Streaming Video InteractionAbstract:Streaming video LLMs must retain evidence before its relevance to future tasks is known and respond when sufficient evidence becomes available. The challenge is to form reusable factual memory without compromising real-time perception. We introduce OneStreamer, which jointly learns query-independent evidence recording and task response through a shared ...

数据来源：HuggingFace Papers Latest Papers1. AgentGarten: Code Worlds for Evolving AgentsAbstract:Interactive virtual worlds allow agents to learn through exploration and interaction. What agents can learn is bounded by the environments they practice in, which must be faithful, with consistent state, rules, and dynamics, and realistic, with observations that follow the real-world visual distributions. Achieving both across diverse worlds remains a bottleneck. We introduce AgentGarten, a framework t ...